Search Results for author:
Found 8 papers, 3 papers with code
From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions
Here, we argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time and incorporate related process data to improve AI predictions in general and human-AI interactions in particular.
The feasibility of artificial consciousness through the lens of neuroscience
Interactions with large language models have led to the suggestion that these models may soon be conscious.
Mind the gap: Challenges of deep learning approaches to Theory of Mind
Theory of Mind is an essential ability of humans to infer the mental states of others.
Perspective Taking in Deep Reinforcement Learning Agents
We believe that, in the long run, building better artificial agents with perspective taking ability can help us develop artificial intelligence that is more human-like and easier to communicate with.
APES: a Python toolbox for simulating reinforcement learning environments
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years.
Do deep reinforcement learning agents model intentions?
In this work we test whether deep reinforcement learning agents explicitly represent other agents' intentions (their specific aims or goals) during a task in which the agents had to coordinate the covering of different spots in a 2D environment.
What deep learning can tell us about higher cognitive functions like mindreading?
Can deep learning (DL) guide our understanding of computations happening in biological brain?
Multiagent Cooperation and Competition with Deep Reinforcement Learning
In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.
